TEST RESULTS AND DISCUSSION Sample Clauses

TEST RESULTS AND DISCUSSION. The algorithm that directly applies single-loop measurements to Equation (2-1) for speed estimates by using a constant g is identified as the “traditional algorithm” in this report. The traditional algorithm and the proposed region growing algorithm were tested against ground truth data for periods of 9, 12, and 15 20-second intervals (3, 4, and 5 minutes, respectively). Graphs of the actual versus plotted values with R2 values are provided in Figure 4-1, and a summary of the results is provided in Table 4-2. A perfect estimation would result in all data points forming a line of slope 1.0 starting at the origin. Therefore, data points falling under the ideal line were underestimated speeds, and those above the line were overestimated speeds. The proposed algorithm, based on the revised region growing concept, clearly provided superior speed estimates. The results of LV estimation are provided in Table 4-3. Comparisons are given in absolute differences for the entire day. Computation of more complex error measurements did not seem appropriate because LV volumes in general constituted less than 10 percent of the traffic at each location and, therefore, could be considered a somewhat “rare” event. Daily LV volume estimates were on average within 4.0 percent of the dual-loop estimated LV volumes. Figure 4-1: Estimated versus Actual Speeds for Region Growing and WSDOT Algorithms with Period Lengths of 3 and 5 Minutes on Lane 2 of Southbound I-5 at NE 145th St, May 17, 2005 Table 4-2: Summary of Speed Estimation Results Traditional Xxxx-Xxxxx Region Growing Algorithm Algorithm Algorithm Station & Loop Code Loop Coeff. Beta Period Length (min) SSE SSE / Period Average % Error SSE SSE / Period Average % Error SSE SSE / Period Average % Error ES-167D _MS 2 1.01 345 38504 24339 16471 80 68 57 11.6% 10.7% 10.1% 12593 6369 4124 26 18 14 6.3% 5.5% 5.1% 00000 0000 0000 00 21 16 6.2% 5.9% 5.3% ES-172R MMS 2 0.92 345 34421 21293 14681 149 59 144 11.1% 10.1% 9.4% 16698 7208 4209 36 20 15 7.2% 6.1% 5.4% 10109 6275 3735 19 17 11 6.2% 5.7% 5.0% ES-209D _MN 2 1.01 345 34650 21713 15815 255 60 257 11.3% 10.4% 9.8% 15265 9800 6550 34 27 25 6.8% 6.5% 6.0% 13421 8808 5309 25 24 17 6.2% 5.8% 5.3% Table 4-3: Summary of LV Volume Estimation Station & Loop Code Period Length (min) Dual-Loop LV Volume Estimated LV Volume Error % Error ES-167D _MS 2 3 4 5 0000 0000 0000 2315 2317 2285 -54 -52 -84 2.28% 2.20% 3.55% ES-172R MMS 2 3 4 5 2566 2566 2566 0000 0000 0000 112 117 156 4.36% 4.56%...
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TEST RESULTS AND DISCUSSION. Cs-137 Fields: The test results for 137Cs are recorded in Tables 2 and 3. Table 2 provides the one- and two-blanket attenuation factors for the point-source geometry, which are the dose rate with the blanket divided by the dose rate without the blanket. Figure 1 shows how the blanket attenuation factors change with angle. Figure 2 shows how the dose rate from the distributed source geometry changes with distance for specific angles. The data points in Figures 1 and 2 are measured data, and this data was fitted with a curve so interpolations and extrapolations could be estimated. Table 2: Cs-137 Point source at 100cm Angle (degrees) Dose rate w/o blanket (μR/h) Dose rate with 1 blanket (μR/h) Dose rate with 2 blankets (μR/h) Attenuation Factors 1 blanket 2 blankets 10 2750±62 950±25 225±15 0.35±0.013 0.08±0.006 20 2850±85 1390±28 450±21 0.49±0.02 0.16±0.009 30 2950±117 1800±37 750±27 0.61±0.031 0.25±0.014 90 3000±106 2150±52 1750±42 0.72±0.034 0.5±0.026 Table 3: Distributed 137Cs sources covered by one blanket at various distances Distance (cm) Dose Rate (μR/h) 30 degrees 45 degrees 70 3750±100 4050±100 100 1850±100 2000±100 150 800±100 900±100 200 475±100 525±100 Blanket attenuation factors versus angle for Cs-137 0.800 0.700 0.600 0.500 0.400 0.300 0.200 0.100 0.000 0 10 20 30 40 50 60 70 80 90 100 Angle relative to floor surface (degrees) fit2 fit 1 2 blankets- raw data 1 blanket - raw data Blanket attenuation factor Figure 1: Blanket attenuation factors versus angle for Cs-137. Distributed Cs-137 sources versus distance and angle 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0 60 85 110 135 160 185 210 Distance from source distribution center (cm) 30 deg 45 deg 30 deg 1/r^2 45 deg 1/r^2 Exposure rate (microR/hr)
Sample 1

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  • Results and Discussion Table 1 (top) shows the root mean square error (RMSE) between the three tests for different numbers of topics. These results show that all three tests largely agree with each other but as the sample size (number of topics) decreases, the agreement decreases. In line with the results found for 50 topics, the randomization and bootstrap tests agree more with the t-test than with each other. We looked at pairwise scatterplots of the three tests at the different topic sizes. While there is some disagreement among the tests at large p-values, i.e. those greater than 0.5, none of the tests would predict such a run pair to have a significant difference. More interesting to us is the behavior of the tests for run pairs with lower p-values. ≥ Table 1 (bottom) shows the RMSE among the three tests for run pairs that all three tests agreed had a p-value greater than 0.0001 and less than 0.5. In contrast to all pairs with p-values 0.0001 (Table 1 top), these run pairs are of more importance to the IR researcher since they are the runs that require a statistical test to judge the significance of the per- formance difference. For these run pairs, the randomization and t tests are much more in agreement with each other than the bootstrap is with either of the other two tests. Looking at scatterplots, we found that the bootstrap tracks the t-test very well but shows a systematic bias to produce p-values smaller than the t-test. As the number of topics de- creases, this bias becomes more pronounced. Figure 1 shows a pairwise scatterplot of the three tests when the number of topics is 10. The randomization test also tends to produce smaller p-values than the t-test for run pairs where the t- test estimated a p-value smaller than 0.1, but at the same time, produces some p-values greater than the t-test’s. As Figure 1 shows, the bootstrap consistently gives smaller p- values than the t-test for these smaller p-values. While the bootstrap and the randomization test disagree with each other more than with the t-test, Figure 1 shows that for a low number of topics, the randomization test shows less noise in its agreement with the bootstrap com- Figure 1: A pairwise comparison of the p-values less than 0.25 produced by the randomization, t-test, and the bootstrap tests for pairs of TREC runs with only 10 topics. The small number of topics high- lights the differences between the three tests. pared to the t-test for small p-values.

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